Asset Ownership by Black and White Families Sharon A. DeVaney, Sophia T. Anong, and Yuan Yang The likelihood of owning homes, investment accounts, and retirement accounts by Black and White families was analyzed using data from the 2004 Survey of Consumer Finances. Education, income, and contact with more financial institutions were almost always influential in the likelihood of owning assets and the value of assets. White families were less likely to be homeowners if they had been denied credit, whereas Black families had less equity in their homes if they had been denied credit. These and other results reinforce the need for financial counselors and planners and consumer educators to help consumers develop a good credit rating, become more risk tolerant, and develop longer horizons for saving and investing. Key Words: asset ownership, Black families, Survey of Consumer Finances, White families
Introduction and Purpose Most families want to increase their ownership of assets and the value of those assets. This study provided insight for educating and advising families by showing what factors are related to the ownership of assets. Wealth is an important measure of economic well-being and, as might be expected, the wealth distribution is not equally distributed by race. Over time the population of the United States has become more diverse. The 2000 Census revealed that the racial and ethnic distribution of the U.S. population was White, 70%; Black, 13%; Hispanic, 12%; Asian and Pacific Islander, 4%; and other, 1% (Stoll, 2004). To limit the scope of the study, only Black and White families were included in this analysis. Two studies of wealth of Black and White families (National Bureau of Economic Research [NBER], 1990; Gittleman & Wolff, 2004) have provided some insight. Authors of both studies noted that the gap in wealth needs further research. Blau and Graham (NBER, 1990) analyzed data on younger families from the National Longitudinal Surveys of Young Men and Young Women. They found that young Black families held 18% of the wealth of young White families. Black families held more of their wealth in homes and vehicles and less in liquid assets or business assets than Whites. Education was the most
important demographic factor affecting the difference in wealth between these younger White and Black families. In a longitudinal study, Gittleman and Wolff (2004) examined data on families from the Panel Study of Income Dynamics using the 1984, 1989, and 1994 waves. On average, White families had higher incomes and higher savings rates, and White families were more likely than Black families to receive inheritances and to receive larger amounts of inheritances. Gittleman and Wolff pointed out that period effects such as changes in the value of housing could influence wealth accumulation differently for families. A considerable amount of research has been devoted to the relationship between tolerance for risk, ethnicity, and asset accumulation (e.g., Coleman, 2003; Gutter & Fontes, 2006; Yao, Gutter, & Hanna, 2005). Coleman (2003) compared attitude toward risk and the amount held in risky assets as a percentage of net worth of Black, White, and Hispanic families in the 1998 Survey of Consumer Finances (SCF). She found that Hispanic respondents were significantly more risk averse than Whites. In regard to Black respondents, she concluded that risk varied depending on the level of net worth. Yao et al. (2005) combined data from the 1983, 1989, 1992, 1995, 1998, and
Sharon A. DeVaney, Ph.D., Professor, Purdue University, Department of Consumer Sciences and Retailing, 812 W. State Street, West Lafayette, IN 47907,
[email protected], (765) 494-8300 Sophia T. Anong, Ph.D., Assistant Professor, Virginia Polytechnic Institute and State University, Department of Apparel, Housing, & Resource Management, 101 Wallace Hall, Blacksburg, VA 24060,
[email protected], (540) 231-8515 Yuan Yang, M.S., Ph.D. Candidate, Purdue University, Department of Consumer Sciences & Retailing, 812 West State Street, West Lafayette, IN 47907,
[email protected], (765) 494-8312
© 2007 Association for Financial Counseling and Planning Education®. All rights of reproduction in any form reserved.
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2001 SCF to compare the levels of financial risk tolerance (from taking no risk to substantial risk) held by Blacks, Hispanics, and Whites. Their primary finding was that Blacks and Hispanics were less willing to take some risk but more willing to take substantial risk. Yao et al. suggested that this effect might be due to variation among minority groups. Gutter and Fontes (2006) investigated the relationship between race and ownership of risky assets (defined as stocks and businesses) as a percentage of total financial assets. They found that Black families were less likely to own risky assets if they had more children, were not working, had less tolerance for risk, and needed more liquidity. However, there was no difference between Black and White families in the proportion of risky assets to net worth when other factors were controlled. In addition to factors such as education and risk tolerance, the accumulation of assets is likely to be related to factors such as age, income, marital status, planning horizon, and other factors. Therefore, a conceptual model is needed to explore the likelihood of owning the assets and the value of the assets. Because the ownership of homes, investment accounts, and retirement accounts are important to the economic well-being of families, the purpose of this study was to investigate the ownership of these assets. The study used data from the 2004 SCF. Although this is a crosssectional data set, it provided the most recent information on assets of Black and White families.
Investments Black families were less likely than White families to hold certain financial assets, especially stocks and transaction accounts (Chiteji & Stafford, 1999; Gutter & Fontes, 2006). Gutter and Fontes (2006) found that Whites were twice as likely as Blacks in the 2004 SCF to own risky assets defined as stock and business assets (56% versus 28%). Chiteji and Stafford (1999) showed that the likelihood of owning transaction accounts and stocks among Black families was influenced by whether their parents held these assets.
Retirement Accounts Previous research has suggested that White families were more likely to be prepared for retirement than Black families (Choudhury, 2001; Yuh, Montalto, & Hanna, 1998). Data from the Health and Retirement Study (HRS) which collected data on families headed by individuals age 51 to 61 showed that 79% of White families had pension accounts compared to 66% of Black families. White families were three times as likely as Black families in the HRS to own Individual Retirement Accounts (IRAs) and Keogh accounts (50% to 15%) (Choudhury, 2001). Yuh et al. (1998) developed a model of retirement wealth and estimated the likelihood of adequacy for families in the 1995 SCF. The descriptive statistics showed that 55% of White families and 39% of Black families would have an adequate amount of retirement wealth. However, there were no differences in retirement wealth adequacy by racial or ethnic background in the logistic regression when other factors were controlled.
Ownership of Assets
Homeownership
Theoretical Framework
Homeownership is a way to transmit wealth from one generation to another. Studies have shown that home ownership leads to stronger families and safer, more closeknit communities with better schools and services. However, owning a home has been a challenge for many Black families. The Home Owner’s Loan Corporation helped many White homeowners avoid default during the Depression, but not Black homeowners (Conley, 2001). This agency instituted ‘redlining’ so those neighborhoods deemed high risk would be assigned a red—no loan— rating. Black neighborhoods received this designation, a practice which was also adopted by private banks. The 1977 Community Reinvestment Act outlawed redlining, and Blacks have had more opportunity to become homeowners (Conley, 2001).
Symbolic Interaction Theory
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The symbolic interaction theory assumes that family background and connections help define, identify, and shape certain values (Ingoldsby, Smith, & Miller, 2004). Boykin and Toms (1985) identified three dimensions of racial socialization among Black families: mainstream experience, minority experience, and Black cultural experience. Boykin and Toms observed that the three themes, respectively, emphasize achievement through education and hard work, presence of racial restrictions, and heritage and historical traditions. Thornton (1997) explored the application of these themes using data from the National Survey of Black Americans. He found that Black parents who were older and parents with more education were more likely to emphasize achievement through education and hard work.
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Life Cycle Hypothesis of Savings The life cycle hypothesis of savings suggested that younger individuals are likely to borrow to finance consumption while they acquire education and professional skills. Hence, they are not likely to accumulate much wealth until they reach midlife and their income has increased. In retirement, they are expected to spend down their accumulated assets (Ando & Modigliani, 1963). However, many older persons have continued to save and many have expected to transfer wealth to the next generation. Thaler and Shefrin (1981) developed the behavioral life cycle hypothesis which suggested that individuals have instincts to be either a planner who is concerned with lifetime utility or a doer who is focused on the present. Thaler and Shefrin also suggested that individuals practice mental accounting, meaning that they have different propensities to save in different categories of accounts. For example, they may think differently about funds in a retirement account than those in a cash reserve for emergencies. Thus, this theoretical framework suggests that individuals could be either long-term or short-term planners. Also, individuals could have a different point of view for different accounts.
Age If the effect for age is consistent with the life cycle hypothesis of savings (Ando & Modigliani, 1963), the value of assets will increase toward midlife. However, it is expected to eventually decrease as assets are drawn down in later life. In this study, the effect of age was examined by including age and age-squared as predictor variables. If the respondent’s saving behavior is consistent with the life cycle hypothesis of savings, the age variable would be positive and the age-squared variable would be negative.
Marital Status Many studies included marital status when analyzing family well-being. For example, Bryant (1990) extended Becker’s (1976) work on the theory of marriage by showing a preference for marriage by single individuals who believed they were better off with two incomes. Also studies showed that couples had higher family incomes and more wealth than single families (Weicher, 1997).
Race In addition to the research focusing on risk tolerance and race, other studies have examined differences in wealth accumulation based on the role of inheritance, income
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differentials, historical periods, social and kin networks, and attitude (Chiteji & Hamilton, 2002; Choudhury, 2001; Gittleman & Wolff, 2004; Straight, 2001). For example, individual characteristics such as race and health status have been associated with community disadvantages such as poor educational facilities and limited access to banking facilities (Robert & Reither, 2004).
Income Family income is the most common measure of the U.S. standard of living. According to Stoll (2004), the Black to White family income ratio was 0.54 in 1990 and 0.58 in 2000. The poverty rate of Black families has historically been at least two to three times higher than that of Whites. Black families’ poverty rate has been particularly sensitive to changes in economic conditions. In 2000, at the height of the economic boom, nearly a quarter of Black families were poor, and nearly 40% of Black female-headed families were poor (Stoll, 2004).
Education There are differences in the attainment of education between Black and White individuals. For men, the Black to White ratio in the attainment of a college education was 0.41 in 1980, whereas in 2000, it was 0.44. For women, the Black/White ratio in the attainment of a college education was 0.66 in 1980 and 0.62 in 2000 (Stoll, 2004). In a study of Black families, 40% of the poorly educated did not own any financial assets, whereas over 80% of families headed by a college graduate controlled some financial assets (Oliver & Shapiro, 2005).
Self--employment Self The self-employed may not accumulate many assets other than their business which is an asset in itself. Also, the self -employed might have a different perspective on investing or on saving for retirement. DeVaney and Kim (2003) found that those who were self-employed were less likely to save for retirement because they planned to work indefinitely. Also, many planned to sell their business when they retired and use that to provide retirement income.
Credit Approval History Access to credit affects homeownership and access to other assets. In the past, Black families have faced barriers to the acquisition of assets such as homes and businesses due to discrimination in mortgage and small business credit markets, customer discrimination, limited access to information about investment opportunities, and other
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factors (NBER, 1998; Munnell, Tootell, Browne, & McEneaney, 1996).
Financial Institutions Financial institutions can help individuals move from debtmanagement to asset-management. Account ownership or doing business with financial institutions was associated with positive financial outcomes (Grable & Joo, 1999). Account ownership increased over time for families across all spectrums of income, net worth, education, race, and age in the 1989, 1992, 1995, 1998, and 2001 SCF data (Hogarth, Anguelov, & Lee, 2005). Therefore, the number of financial institutions that one does business with could have positively influenced the likelihood of owning assets.
Saving Behavior and Planning Horizon Minority heads of families were more likely to contribute to family functions, family care-giving, and socialization than families with a White head (Aranda & Knight, 1997). In a study in which savings motives were organized as a hierarchy, there were only two significant differences between White and minority families. White families were less likely than minority families of all races to move up the hierarchy to higher level savings motives such as societal, luxury, and self-actualization from saving for basic needs and from saving for security needs (DeVaney, Anong, & Whirl, 2007). It is not known whether Black and White families differ in their preference for the length of planning horizons for saving and investing.
ing professional advice are all expected to influence the likelihood of owning assets and the value of the assets. It is hypothesized that there will be differences between Black and White families in the likelihood of ownership and the amount of the various assets. Methods
Data and Sample The study used data from the 2004 SCF to assess the likelihood of asset ownership and the amount of the assets of Black and White families. The SCF is collected every 3 years by the National Organization for Research at the University of Chicago (Kennickell, 2005). Sponsored by the Board of Governors of the Federal Reserve System, the SCF provides detailed information on demographic characteristics and assets and liabilities of U.S. families. The sample for this study consisted of 481 Black families and 3,468 White families in the 2004 SCF. Families of other racial and ethnic backgrounds were excluded. A multiple imputation technique was used to handle missing and incomplete data and five implicates of the data were developed (Rubin, 1987). All five implicates were used in the study. The Repeated Imputation Inference (RII) method was used in all of the analyses including descriptive analysis, logistic regressions, and tobit regressions (Kennickell, 1997). A weight variable was applied for the descriptive analysis.
Dependent Variables and Analysis Professional Advice Some studies have shown conflicting results in regard to the relationship between professional advice and asset ownership. In a study of clerical workers, Grable and Joo (1999) found that age was important, but race, education, gender, marital status, and income did not affect the decision to seek professional advice. A study with data from the 1998 SCF showed that those who sought professional advice for saving and investing and credit and borrowing had higher income, net worth, and financial assets (Elmerick, Montalto, & Fox, 2002). Elmerick et al. (2002) also found that Black families were significantly more likely to seek professional advice on saving and investing and on credit and borrowing compared to White families when all other factors were controlled. In summary, factors such as age, marital status, race, income, education, self-employment, credit approval history, con-tacts with financial institutions, willingness to take risk, saving behavior, planning horizon, and seek-
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The ownership of each of three assets (homes, investment accounts, and retirement accounts) was examined based on a conceptual model that included race, age, marital status, education, family income, self-employment, denial of credit, contacts with financial institutions, willingness to take risk, saving behavior, planning horizon, and seeking professional advice. The likelihood of owning each type of asset was examined using logistic regression. Ownership was coded as 1 if the asset was owned and 0 otherwise. Logistic regression is the appropriate method when the dependent variable is binary (Kennedy, 1998). Next, the value of the assets was examined using tobit regression. This is the appropriate method when a large percentage of the continuous dependent variables are 0 (Kleinbaum, Kupper, Muller, & Nizam, 1998). The dependent variables for each of the tobit regressions were the value of each of the assets. Housing equity was
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measured by subtracting the amount still owed on the mortgage from the current market value of the home. The value of investment accounts consisted of the amount in savings bonds, certificates of deposit, money market accounts, call accounts, stocks, and bonds or mutual funds that were not included in retirement accounts. The value in retirement accounts consisted of the amount in retirement accounts (such as defined contribution accounts), IRAs, and Keogh accounts.
Independent Variables The independent variables consisted of socioeconomic and behavioral factors. Socioeconomic factors included the age, marital status, race, and education of the head of the family; family income; whether the head was selfemployed or had been denied credit; and the number of financial institutions with which the family had an account or did personal financial business on a regular basis. Marital status was coded as 1 if the head was married and 0 if the head was single, divorced, separated, or widowed. Income was the family’s annual income. The log of income was used for the regression analyses to reduce heteroskedasticity. Denial of credit was measured by the response to this twopart question: “In the past 5 years, has a particular lender or creditor turned down any request you made for credit or not given you as much credit as you applied for?” The responses, “Yes, turned down” and “Yes, not as much credit,” were coded as 1. If the family had been approved for credit, denial of credit was coded as 0. Behavioral factors included risk tolerance, being a regular saver, planning horizon for saving and investing, and seeking professional advice for saving and investing. Risk tolerance was measured from 1, not willing to take any financial risk, to 4, take substantial financial risk expecting to earn substantial returns. The variable was recoded as (a) above average risk or substantial risk to equal high risk, (b) average risk, and (c) no risk. “No risk” was the reference category. Saving behavior was measured by this statement, “(I/we) save regularly by putting money aside each month.” In the SCF, family heads were asked about their preference for length of the planning horizon for saving and investing. The possible responses were “a few months,” “a year,” “a few years,” “5 to 10 years,” or “more than 10 years.” “A few months” was the reference category. If the family head sought professional advice for saving and investing
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from advisors including accountants, bankers, brokers, financial planners, or lawyers, the variable was coded as 1 and 0 otherwise. See Table 1 for the coding of variables. Descriptive Statistics A typical Black family was headed by an individual age 47 with almost 13 years of education. Twenty-six percent were married. The average annual family income was $38,010. Six percent were self-employed, 30% had been denied credit, 41% were regular savers, 57% were not willing to take risk when saving or investing, 39% had sought professional advice, and most preferred shorter planning horizons. On average, Black families had accounts with 1.63 financial institutions. A typical White family was headed by an individual age 51 with almost 14 years of education. Fifty-five percent were married. Average family income was $76,960. Among White families, 13% were self-employed, 19% had been denied credit, 42% were regular savers, and 37% would not take any risk when making saving or investing decisions. Among White families, the planning horizons were somewhat longer, and 54% had sought professional advice. On average, White families had accounts with 2.5 financial institutions. The percentage of Black families who owned each type of asset was as follows: a home, 45%; an investment account, 32%; and a retirement account, 31%. For White families, the percentage who owned each type of asset was as follows: homes, 70%; investment accounts, 61%; and retirement accounts, 55%. The average equity for each of these assets for Black families was as follows: a home, $39,294; investments, $8,817; and retirement accounts, $18,187. The average equity of these assets for White families was as follows: homes, $133,087; investments, $119,513; and retirement accounts, $72,219 (see Table 1). Likelihood of Owning Assets For each of the types of assets, the likelihood of owning the asset or the equity in the asset was estimated for the total sample. If there was a statistically significant effect for race, the next step was to conduct separate regressions for Black and White families for each type of asset. There were significant differences for each type of asset and regressions were conducted separately for the Black and White samples (see Tables 2 through 4). The likelihood of owning each of the assets is discussed first followed by a discussion of factors that influenced the value of each asset.
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Table 1. Measurement of Variables and Weighted Descriptive Statistics of Black Families (n = 481) and White Families (n = 3,468) in the 2004 Survey of Consumer Finances Variables
Total (N = 3,949) M (SD ) or %
Measurement
Dependent variables 65.96%
Blacks (n = 481) M (SD ) or %
Whites (n = 3,468) M (SD ) or %
45.02%
69.84%
Homeownership
1 = homeowner; 0 = otherwise
Investment ownership
1 = investment holder; 0 = otherwise
56.50%
32.64%
60.91%
Retirement account ownership
1 = account holder; 0 = otherwise
51.47%
31.26%
55.21%
Home equity Value of investments
Continuous, $ Continuous, $
118,432 (290,516) 102,216 (1,048,004)
39,294 (77,138) 8,817 (121,386)
133,087 (312,335) 119,513 (1,138,886)
Retirement savings
Continuous, $
63,777 (223,661)
18,187 (75,739)
72,219 (240,349)
50.24 (17.37) 50.18%
47.37 (16.73) 25.69%
50.77 (17.44) 54.71%
1 = yes; 0 = otherwise 1 = yes; 0 = otherwise Continuous, highest grade Continuous; log of income 1 = self-employed; 0 = otherwise
15.63% 84.37% 13.52 (2.60) 70,874 (223,199) 12.18%
12.760 (2.71) 38,010 (105,647) 5.59%
13.67 (2.55) 76,960 (238,201) 13.40%
1 = denied/limited credit; 0 = otherwise
20.37%
30.21%
18.55%
2.36 (1.60)
1.63 (1.36)
2.50 (1.61)
1 = yes; 0 = no 1 = yes; 0 = no 1 = yes; 0 = no 1 = save regularly; 0 = otherwise
39.81% 40.67% 19.53% 41.46%
57.11% 28.19% 14.70% 40.84%
36.60% 42.98% 20.42% 41.57%
1 = yes; 0 = no 1 = yes; 0 = no
17.91% 13.43%
26.19% 16.65%
16.38% 12.81%
Few years
1 = yes; 0 = no
27.94%
28.12%
27.91%
5 to 10 years
1 = yes; 0 = no
26.40%
21.87%
27.23%
More than 10 years
1 = yes; 0 = no
14.32%
7.17%
15.65%
51.48%
38.92%
53.80%
Independent variables Socioeconomic variables Age Marital status Race Black White Education Family income Self-employment Denied credit Financial institutions Behavioral variables Risk tolerance Take no risk Take average risk Take high risk Regular saver Planning horizon Few months A year
Continuous, years 1 = married; 0 = otherwise
Continuous
Seek professional advice 1 = seek advice from professional; 0 = otherwise
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Homeownership
Those who were married, who saved regularly, and who sought professional advice were more likely to be homeowners. Compared to those who were willing to take high risk, those who were not willing to take risk were less likely to be homeowners. Those who had been denied credit were less likely to be homeowners (see Table 2).
Among Black family heads, years of education and number of financial institutions with whom the respondent had regular contact were positively related to the likelihood of homeownership. Also, those who were married were more likely to be homeowners than those who were single, never married, divorced or separated, or widowed.
Investment Accounts Among White respondents, the likelihood of homeownership increased until age 82, and then it began to decrease. The likelihood of homeownership increased as education, income, and contact with financial institutions increased.
The likelihood of holding investment accounts increased for Black families as education and contact with financial institutions increased. The likelihood of holding investment accounts for White families increased with more
Table 2. Results of the Regressions on Homeownership and Equity of Blacks (n = 481) and Whites (n = 3,468) in the 2004 Survey of Consumer Finances Homeownership Independent variables
Parameter estimate Age Age squared
0.0758 -0.0001
Home equity Whites
Blacks
Blacks
Odds ratio
Parameter estimate
Odds ratio
Parameter estimate
1.079 1.000
0.1194*** -0.0007***
1.127 0.999
5773.9 -4.64680
Whites
p .1078 .8905
Parameter estimate 50092.0*** -267.4*
p .0002 .0251
Married (reference group: unmarried) Log income Education
0.8203** 0.2433 0.1424**
2.271 1.275 1.153
1.0827*** 0.2778*** 0.0496*
2.953 1.320 1.051
62960.9** 31165.8*** 12373.6**
.0040 .0007 .0021
155593.0* .0320 574157.0***
